Computerized systems and methods for product categorization using artificial intelligence

ABSTRACT

Systems and methods are provided for categorizing products using Al. One method comprises retrieving initial training data including products associated with one or more categories; pre-processing the initial training data to generate synthesized training data; generating a hierarchical model using the synthesized training data, the hierarchical model containing at least two layers of nodes below a root node; receiving information associated with a first uncategorized product; and receiving a request to predict a set of N categories with the highest N total probability scores. The method may further comprise predicting, using the hierarchical model, N categories of the first uncategorized product, by calculating total probability scores, and determining the N categories with the highest N total probability scores; sorting the first uncategorized product into the N categories associated with the nodes from the first and second layers having the highest total probability scores; and displaying the sorted first uncategorized product and its associated N categories on a user device associated with a user.

TECHNICAL FIELD

The present disclosure generally relates to computerized systems andmethods for product categorization using artificial intelligence. Inparticular, embodiments of the present disclosure relate to inventiveand unconventional systems relate to receiving training data associatedwith one or more categories, pre-processing the received training data,training one or more models for product categorization, and usinginformation associated with uncategorized products received from sellersto predict and sort the uncategorized products into correct categories.

BACKGROUND

Consumers often shop for and purchase various items online throughcomputers and smart devices. These online shoppers often rely onsearching through categories of products to find products to purchase.However, the normal online shopping experience is hindered byincorrectly categorized products.

Millions of products are registered online by sellers every day. Sellersare required to select the correct category to which their productbelongs when registering their products online for sale. However, manysellers do not select the correct category when registering theirproduct. For example, a seller may incorrectly select the “Kid'sFashion” category when registering an infant onesie that belongs in the“Baby” category. Incorrect product categorization may severely reduce aconsumer's user experience by prolonging the consumer's product searchand by reducing the recommendation quality of the online platform.Furthermore, manually correcting the categorization of products is oftendifficult and time-consuming since over 17,000 different categories mayexist. A consumer's user experience would be significantly improved ifthe online platform automatically categorized products into theircorrect categories.

Therefore, there is a need for improved methods and systems for productcategorization so that consumers may quickly find and purchase productswhile online shopping.

SUMMARY

One aspect of the present disclosure is directed to acomputer-implemented system for Al-based product categorization. Thesystem may comprise at least one processor; and at least onenon-transitory storage medium comprising instructions that, whenexecuted by the at least one processor, cause the at least one processorto perform steps. The steps may comprise retrieving initial trainingdata including products associated with one or more categories;pre-processing the initial training data to generate synthesizedtraining data; generating a hierarchical model using the synthesizedtraining data, the hierarchical model containing at least two layers ofnodes below a root node; receiving information associated with a firstuncategorized product; and receiving a request to predict a set of Ncategories with the highest N total probability scores. The steps mayfurther comprise predicting, using the hierarchical model, N categoriesof the first uncategorized product, by calculating a probability scorefor each node on the first layer for the first uncategorized product,determining a set of n nodes on the first layer with the highest nprobability scores, calculating a probability score for a set of nodeson the second layer, the set of nodes being below the determined set ofn nodes with the highest n scores, determining a set of m nodes on thesecond layer with the highest m scores, calculating a total probabilityscore based on the probability scores of the m nodes with the highest mscores and respective n nodes in the first layer, and determining the Ncategories with the highest N total probability scores; sorting thefirst uncategorized product into the N categories associated with thenodes from the first and second layers having the highest totalprobability scores; and displaying the sorted first uncategorizedproduct and its associated N categories on a user device associated witha user.

Another aspect of the present disclosure is directed to a method forcategorizing products using Al. The method may comprise retrievinginitial training data including products associated with one or morecategories; pre-processing the initial training data to generatesynthesized training data; generating a hierarchical model using thesynthesized training data, the hierarchical model containing at leasttwo layers of nodes below a root node; receiving information associatedwith a first uncategorized product; and receiving a request to predict aset of N categories with the highest N total probability scores. Themethod may further comprise predicting, using the hierarchical model, Ncategories of the first uncategorized product, by calculating aprobability score for each node on the first layer for the firstuncategorized product, determining a set of n nodes on the first layerwith the highest n probability scores, calculating a probability scorefor a set of nodes on the second layer, the set of nodes being below thedetermined set of n nodes with the highest n scores, determining a setof m nodes on the second layer with the highest m scores, calculating atotal probability score based on the probability scores of the m nodeswith the highest m scores and respective n nodes in the first layer, anddetermining the N categories with the highest N total probabilityscores; and sorting the first uncategorized product into the Ncategories associated with the nodes from the first and second layershaving the highest total probability scores.

Yet another aspect of the present disclosure is directed to acomputer-implemented system for Al-based product categorization. Thesystem may comprise at least one processor; and at least onenon-transitory storage medium comprising instructions that, whenexecuted by the at least one processor, cause the at least one processorto perform steps. The steps may comprise retrieving initial trainingdata including products and images associated with one or morecategories; pre-processing the initial training data to generatesynthesized training data; generating a hierarchical model using thesynthesized training data, the hierarchical model containing at leasttwo layers of nodes below a root node; generating an image model usingthe synthesized training data; receiving information associated with afirst uncategorized product; and receiving a request to predict a set ofN categories with the highest N total probability scores and a requestto predict a set of M categories with the highest M total probabilityscores. The steps may further comprise predicting, using thehierarchical model, N categories of the first uncategorized product, bycalculating a probability score for each node on the first layer for thefirst uncategorized product, determining a set of n nodes on the firstlayer with the highest n probability scores, calculating a probabilityscore for a set of nodes on the second layer, the set of nodes beingbelow the determined set of n nodes with the highest n scores,determining a set of m nodes on the second layer with the highest mscores, calculating a total probability score based on the probabilityscores of the m nodes with the highest m scores and respective n nodesin the first layer, and determining the N categories with the highest Ntotal probability scores. The steps may further comprise predicting,using the image model, M categories of the first uncategorized product,by calculating a probability score for the first uncategorized productand determining the M categories with the highest M total probabilityscores. The steps may further comprise averaging the total probabilityscore of the N categories with the M categories; sorting the firstuncategorized product into the category of the N or M categories havingthe highest averaged total probability score; and displaying the sortedfirst uncategorized product and its associated N or M categories on auser device associated with a user.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations, consistent with the disclosed embodiments.

FIG. 1B depicts a sample Search Result Page (SRP) that includes one ormore search results satisfying a search request along with interactiveuser interface elements, consistent with the disclosed embodiments.

FIG. 1C depicts a sample Single Display Page (SDP) that includes aproduct and information about the product along with interactive userinterface elements, consistent with the disclosed embodiments.

FIG. 1D depicts a sample Cart page that includes items in a virtualshopping cart along with interactive user interface elements, consistentwith the disclosed embodiments.

FIG. 1E depicts a sample Order page that includes items from the virtualshopping cart along with information regarding purchase and shipping,along with interactive user interface elements, consistent with thedisclosed embodiments.

FIG. 2 is a diagrammatic illustration of an exemplary fulfillment centerconfigured to utilize disclosed computerized systems, consistent withthe disclosed embodiments.

FIG. 3 is a schematic block diagram illustrating an exemplary embodimentof a network comprising computerized systems for training one or moremodels for product categorization, consistent with the disclosedembodiments.

FIG. 4 depicts sample training data for product categorization,consistent with the disclosed embodiments.

FIG. 5 is a schematic block diagram illustrating an exemplary embodimentof a network comprising computerized systems for product categorizationusing one or more models, consistent with the disclosed embodiments.

FIG. 6 depicts a sample hierarchical model for product categorization,consistent with the disclosed embodiments.

FIG. 7 depicts a process for product categorization, consistent with thedisclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents and steps illustrated in the drawings, and the illustrativemethods described herein may be modified by substituting, reordering,removing, or adding steps to the disclosed methods. Accordingly, thefollowing detailed description is not limited to the disclosedembodiments and examples. Instead, the proper scope of the invention isdefined by the appended claims.

Embodiments of the present disclosure are directed to systems andmethods configured for product categorization using artificialintelligence. The disclosed embodiments are advantageously capable ofautomatically generating training data for training a machine learningmodel (“model”), generating the model, and using the model to correctlycategorize products of sellers. For example, a training data system mayautomatically generate initial training data including productsassociated with one or more categories. The initial training data mayinclude automatically generated virtual product data, automaticallygenerated mapping guideline keyword data, or automatic selection of liveproducts. In some embodiments, the initial training data may includehuman labeled data provided by internal users (e.g., employees).

In one implementation, a pre-processing system may pre-process theinitial training data received from training data system to generatesynthesized training data. For example, text-based initial training datamay be pre-processed using any combination of methods, including stopword elimination, keyword tokenization, deduplication of keywords, andaugmentation of the initial training data, and image-based initialtraining data may be pre-processed using image augmentation techniques(e.g., PyTorch). A hierarchical model trainer system may receive thetext-based synthesized training data generated by the pre-processingsystem and an image model trainer system may receive the image-basedsynthesized training data generated by the pre-processing system. Thehierarchical model trainer system and the image model trainer maygenerate and train at least one hierarchical model and at least oneimage model, respectively, using the received synthesized data forproduct categorization.

In some embodiments, a product category predictor may receiveinformation associated with a first uncategorized product. For example,a seller may be prompted to enter a concatenated text string includingthe product name, attribute values, manufacturer, brand, and modelnumber when attempting to register a product. The product categorypredictor may receive a request to predict a number of categories withthe highest total probability scores. The product category predictor mayuse the hierarchical model to predict the most relevant categories ofthe first uncategorized product by recursively calculating probabilityscores of potential categories and subcategories. The product categorypredictor may subsequently sort the uncategorized product into one ormore of the categories having the highest total probability scores.

Referring to FIG. 1A, a schematic block diagram 100 illustrating anexemplary embodiment of a system comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations is shown. As illustrated in FIG. 1A, system 100 may include avariety of systems, each of which may be connected to one another viaone or more networks. The systems may also be connected to one anothervia a direct connection, for example, using a cable. The depictedsystems include a shipment authority technology (SAT) system 101, anexternal front end system 103, an internal front end system 105, atransportation system 107, mobile devices 107A, 107B, and 107C, sellerportal 109, shipment and order tracking (SOT) system 111, fulfillmentoptimization (FO) system 113, fulfillment messaging gateway (FMG) 115,supply chain management (SCM) system 117, warehouse management system119, mobile devices 119A, 119B, and 119C (depicted as being inside offulfillment center (FC) 200), 3^(rd) party fulfillment systems 121A,121B, and 121C, fulfillment center authorization system (FC Auth) 123,and labor management system (LMS) 125.

SAT system 101, in some embodiments, may be implemented as a computersystem that monitors order status and delivery status. For example, SATsystem 101 may determine whether an order is past its Promised DeliveryDate (PDD) and may take appropriate action, including initiating a neworder, reshipping the items in the non-delivered order, canceling thenon-delivered order, initiating contact with the ordering customer, orthe like. SAT system 101 may also monitor other data, including output(such as a number of packages shipped during a particular time period)and input (such as the number of empty cardboard boxes received for usein shipping). SAT system 101 may also act as a gateway between differentdevices in system 100, enabling communication (e.g., usingstore-and-forward or other techniques) between devices such as externalfront end system 103 and FO system 113.

External front end system 103, in some embodiments, may be implementedas a computer system that enables external users to interact with one ormore systems in system 100. For example, in embodiments where system 100enables the presentation of systems to enable users to place an orderfor an item, external front end system 103 may be implemented as a webserver that receives search requests, presents item pages, and solicitspayment information. For example, external front end system 103 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, external front end system 103may run custom web server software designed to receive and processrequests from external devices (e.g., mobile device 102A or computer102B), acquire information from databases and other data stores based onthose requests, and provide responses to the received requests based onacquired information.

In some embodiments, external front end system 103 may include one ormore of a web caching system, a database, a search system, or a paymentsystem. In one aspect, external front end system 103 may comprise one ormore of these systems, while in another aspect, external front endsystem 103 may comprise interfaces (e.g., server-to-server,database-to-database, or other network connections) connected to one ormore of these systems.

An illustrative set of steps, illustrated by FIGS. 1B, 1C, 1D, and 1E,will help to describe some operations of external front end system 103.External front end system 103 may receive information from systems ordevices in system 100 for presentation and/or display. For example,external front end system 103 may host or provide one or more web pages,including a Search Result Page (SRP) (e.g., FIG. 1B), a Single DetailPage (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1D), or an Orderpage (e.g., FIG. 1E). A user device (e.g., using mobile device 102A orcomputer 102B) may navigate to external front end system 103 and requesta search by entering information into a search box. External front endsystem 103 may request information from one or more systems in system100. For example, external front end system 103 may request informationfrom FO System 113 that satisfies the search request. External front endsystem 103 may also request and receive (from FO System 113) a PromisedDelivery Date or “PDD” for each product included in the search results.The PDD, in some embodiments, may represent an estimate of when apackage containing the product will arrive at the user's desiredlocation or a date by which the product is promised to be delivered atthe user's desired location if ordered within a particular period oftime, for example, by the end of the day (11:59 PM). (PDD is discussedfurther below with respect to FO System 113.)

External front end system 103 may prepare an SRP (e.g., FIG. 1B) basedon the information. The SRP may include information that satisfies thesearch request. For example, this may include pictures of products thatsatisfy the search request. The SRP may also include respective pricesfor each product, or information relating to enhanced delivery optionsfor each product, PDD, weight, size, offers, discounts, or the like.External front end system 103 may send the SRP to the requesting userdevice (e.g., via a network).

A user device may then select a product from the SRP, e.g., by clickingor tapping a user interface, or using another input device, to select aproduct represented on the SRP. The user device may formulate a requestfor information on the selected product and send it to external frontend system 103. In response, external front end system 103 may requestinformation related to the selected product. For example, theinformation may include additional information beyond that presented fora product on the respective SRP. This could include, for example, shelflife, country of origin, weight, size, number of items in package,handling instructions, or other information about the product. Theinformation could also include recommendations for similar products(based on, for example, big data and/or machine learning analysis ofcustomers who bought this product and at least one other product),answers to frequently asked questions, reviews from customers,manufacturer information, pictures, or the like.

External front end system 103 may prepare an SDP (Single Detail Page)(e.g., FIG. 1C) based on the received product information. The SDP mayalso include other interactive elements such as a “Buy Now” button, a“Add to Cart” button, a quantity field, a picture of the item, or thelike. The SDP may further include a list of sellers that offer theproduct. The list may be ordered based on the price each seller offerssuch that the seller that offers to sell the product at the lowest pricemay be listed at the top. The list may also be ordered based on theseller ranking such that the highest ranked seller may be listed at thetop. The seller ranking may be formulated based on multiple factors,including, for example, the seller's past track record of meeting apromised PDD. External front end system 103 may deliver the SDP to therequesting user device (e.g., via a network).

The requesting user device may receive the SDP which lists the productinformation. Upon receiving the SDP, the user device may then interactwith the SDP. For example, a user of the requesting user device mayclick or otherwise interact with a “Place in Cart” button on the SDP.This adds the product to a shopping cart associated with the user. Theuser device may transmit this request to add the product to the shoppingcart to external front end system 103.

External front end system 103 may generate a Cart page (e.g., FIG. 1D).The Cart page, in some embodiments, lists the products that the user hasadded to a virtual “shopping cart.” A user device may request the Cartpage by clicking on or otherwise interacting with an icon on the SRP,SDP, or other pages. The Cart page may, in some embodiments, list allproducts that the user has added to the shopping cart, as well asinformation about the products in the cart such as a quantity of eachproduct, a price for each product per item, a price for each productbased on an associated quantity, information regarding PDD, a deliverymethod, a shipping cost, user interface elements for modifying theproducts in the shopping cart (e.g., deletion or modification of aquantity), options for ordering other product or setting up periodicdelivery of products, options for setting up interest payments, userinterface elements for proceeding to purchase, or the like. A user at auser device may click on or otherwise interact with a user interfaceelement (e.g., a button that reads “Buy Now”) to initiate the purchaseof the product in the shopping cart. Upon doing so, the user device maytransmit this request to initiate the purchase to external front endsystem 103.

External front end system 103 may generate an Order page (e.g., FIG. 1E)in response to receiving the request to initiate a purchase. The Orderpage, in some embodiments, re-lists the items from the shopping cart andrequests input of payment and shipping information. For example, theOrder page may include a section requesting information about thepurchaser of the items in the shopping cart (e.g., name, address, e-mailaddress, phone number), information about the recipient (e.g., name,address, phone number, delivery information), shipping information(e.g., speed/method of delivery and/or pickup), payment information(e.g., credit card, bank transfer, check, stored credit), user interfaceelements to request a cash receipt (e.g., for tax purposes), or thelike. External front end system 103 may send the Order page to the userdevice.

The user device may enter information on the Order page and click orotherwise interact with a user interface element that sends theinformation to external front end system 103. From there, external frontend system 103 may send the information to different systems in system100 to enable the creation and processing of a new order with theproducts in the shopping cart.

In some embodiments, external front end system 103 may be furtherconfigured to enable sellers to transmit and receive informationrelating to orders.

Internal front end system 105, in some embodiments, may be implementedas a computer system that enables internal users (e.g., employees of anorganization that owns, operates, or leases system 100) to interact withone or more systems in system 100. For example, in embodiments wheresystem 100 enables the presentation of systems to enable users to placean order for an item, internal front end system 105 may be implementedas a web server that enables internal users to view diagnostic andstatistical information about orders, modify item information, or reviewstatistics relating to orders. For example, internal front end system105 may be implemented as a computer or computers running software suchas the Apache HTTP Server, Microsoft Internet Information Services(IIS), NGINX, or the like. In other embodiments, internal front endsystem 105 may run custom web server software designed to receive andprocess requests from systems or devices depicted in system 100 (as wellas other devices not depicted), acquire information from databases andother data stores based on those requests, and provide responses to thereceived requests based on acquired information.

In some embodiments, internal front end system 105 may include one ormore of a web caching system, a database, a search system, a paymentsystem, an analytics system, an order monitoring system, or the like. Inone aspect, internal front end system 105 may comprise one or more ofthese systems, while in another aspect, internal front end system 105may comprise interfaces (e.g., server-to-server, database-to-database,or other network connections) connected to one or more of these systems.

Transportation system 107, in some embodiments, may be implemented as acomputer system that enables communication between systems or devices insystem 100 and mobile devices 107A-107C. Transportation system 107, insome embodiments, may receive information from one or more mobiledevices 107A-107C (e.g., mobile phones, smart phones, PDAs, or thelike). For example, in some embodiments, mobile devices 107A-107C maycomprise devices operated by delivery workers. The delivery workers, whomay be permanent, temporary, or shift employees, may utilize mobiledevices 107A-107C to effect delivery of packages containing the productsordered by users. For example, to deliver a package, the delivery workermay receive a notification on a mobile device indicating which packageto deliver and where to deliver it. Upon arriving at the deliverylocation, the delivery worker may locate the package (e.g., in the backof a truck or in a crate of packages), scan or otherwise capture dataassociated with an identifier on the package (e.g., a barcode, an image,a text string, an RFID tag, or the like) using the mobile device, anddeliver the package (e.g., by leaving it at a front door, leaving itwith a security guard, handing it to the recipient, or the like). Insome embodiments, the delivery worker may capture photo(s) of thepackage and/or may obtain a signature using the mobile device. Themobile device may send information to transportation system 107including information about the delivery, including, for example, time,date, GPS location, photo(s), an identifier associated with the deliveryworker, an identifier associated with the mobile device, or the like.Transportation system 107 may store this information in a database (notpictured) for access by other systems in system 100. Transportationsystem 107 may, in some embodiments, use this information to prepare andsend tracking data to other systems indicating the location of aparticular package.

In some embodiments, certain users may use one kind of mobile device(e.g., permanent workers may use a specialized PDA with custom hardwaresuch as a barcode scanner, stylus, and other devices) while other usersmay use other kinds of mobile devices (e.g., temporary or shift workersmay utilize off-the-shelf mobile phones and/or smartphones).

In some embodiments, transportation system 107 may associate a user witheach device. For example, transportation system 107 may store anassociation between a user (represented by, e.g., a user identifier, anemployee identifier, or a phone number) and a mobile device (representedby, e.g., an International Mobile Equipment Identity (IMEI), anInternational Mobile Subscription Identifier (IMSI), a phone number, aUniversal Unique Identifier (UUID), or a Globally Unique Identifier(GUID)). Transportation system 107 may use this association inconjunction with data received on deliveries to analyze data stored inthe database in order to determine, among other things, a location ofthe worker, an efficiency of the worker, or a speed of the worker.

Seller portal 109, in some embodiments, may be implemented as a computersystem that enables sellers or other external entities to electronicallycommunicate with one or more systems in system 100. For example, aseller may utilize a computer system (not pictured) to upload or provideproduct information, order information, contact information, or thelike, for products that the seller wishes to sell through system 100using seller portal 109.

Shipment and order tracking system 111, in some embodiments, may beimplemented as a computer system that receives, stores, and forwardsinformation regarding the location of packages containing productsordered by customers (e.g., by a user using devices 102A-102B). In someembodiments, shipment and order tracking system 111 may request or storeinformation from web servers (not pictured) operated by shippingcompanies that deliver packages containing products ordered bycustomers.

In some embodiments, shipment and order tracking system 111 may requestand store information from systems depicted in system 100. For example,shipment and order tracking system 111 may request information fromtransportation system 107. As discussed above, transportation system 107may receive information from one or more mobile devices 107A-107C (e.g.,mobile phones, smart phones, PDAs, or the like) that are associated withone or more of a user (e.g., a delivery worker) or a vehicle (e.g., adelivery truck). In some embodiments, shipment and order tracking system111 may also request information from warehouse management system (WMS)119 to determine the location of individual products inside of afulfillment center (e.g., fulfillment center 200). Shipment and ordertracking system 111 may request data from one or more of transportationsystem 107 or WMS 119, process it, and present it to a device (e.g.,user devices 102A and 102B) upon request.

Fulfillment optimization (FO) system 113, in some embodiments, may beimplemented as a computer system that stores information for customerorders from other systems (e.g., external front end system 103 and/orshipment and order tracking system 111). FO system 113 may also storeinformation describing where particular items are held or stored. Forexample, certain items may be stored only in one fulfillment center,while certain other items may be stored in multiple fulfillment centers.In still other embodiments, certain fulfillment centers may be designedto store only a particular set of items (e.g., fresh produce or frozenproducts). FO system 113 stores this information as well as associatedinformation (e.g., quantity, size, date of receipt, expiration date,etc.).

FO system 113 may also calculate a corresponding PDD (promised deliverydate) for each product. The PDD, in some embodiments, may be based onone or more factors. For example, FO system 113 may calculate a PDD fora product based on a past demand for a product (e.g., how many timesthat product was ordered during a period of time), an expected demandfor a product (e.g., how many customers are forecast to order theproduct during an upcoming period of time), a network-wide past demandindicating how many products were ordered during a period of time, anetwork-wide expected demand indicating how many products are expectedto be ordered during an upcoming period of time, one or more counts ofthe product stored in each fulfillment center 200, which fulfillmentcenter stores each product, expected or current orders for that product,or the like.

In some embodiments, FO system 113 may determine a PDD for each producton a periodic basis (e.g., hourly) and store it in a database forretrieval or sending to other systems (e.g., external front end system103, SAT system 101, shipment and order tracking system 111). In otherembodiments, FO system 113 may receive electronic requests from one ormore systems (e.g., external front end system 103, SAT system 101,shipment and order tracking system 111) and calculate the PDD on demand.

Fulfillment messaging gateway (FMG) 115, in some embodiments, may beimplemented as a computer system that receives a request or response inone format or protocol from one or more systems in system 100, such asFO system 113, converts it to another format or protocol, and forward itin the converted format or protocol to other systems, such as WMS 119 or3^(rd) party fulfillment systems 121A, 121B, or 121C, and vice versa.

Supply chain management (SCM) system 117, in some embodiments, may beimplemented as a computer system that performs forecasting functions.For example, SCM system 117 may forecast a level of demand for aparticular product based on, for example, a past demand for products, anexpected demand for a product, a network-wide past demand, anetwork-wide expected demand, a count products stored in eachfulfillment center 200, expected or current orders for each product, orthe like. In response to this forecasted level and the amount of eachproduct across all fulfillment centers, SCM system 117 may generate oneor more purchase orders to purchase and stock a sufficient quantity tosatisfy the forecasted demand for a particular product.

Warehouse management system (WMS) 119, in some embodiments, may beimplemented as a computer system that monitors workflow. For example,WMS 119 may receive event data from individual devices (e.g., devices107A-107C or 119A-119C) indicating discrete events. For example, WMS 119may receive event data indicating the use of one of these devices toscan a package. As discussed below with respect to fulfillment center200 and FIG. 2, during the fulfillment process, a package identifier(e.g., a barcode or RFID tag data) may be scanned or read by machines atparticular stages (e.g., automated or handheld barcode scanners, RFIDreaders, high-speed cameras, devices such as tablet 119A, mobiledevice/PDA 1198, computer 119C, or the like). WMS 119 may store eachevent indicating a scan or a read of a package identifier in acorresponding database (not pictured) along with the package identifier,a time, date, location, user identifier, or other information, and mayprovide this information to other systems (e.g., shipment and ordertracking system 111).

WMS 119, in some embodiments, may store information associating one ormore devices (e.g., devices 107A-107C or 119A-119C) with one or moreusers associated with system 100. For example, in some situations, auser (such as a part- or full-time employee) may be associated with amobile device in that the user owns the mobile device (e.g., the mobiledevice is a smartphone). In other situations, a user may be associatedwith a mobile device in that the user is temporarily in custody of themobile device (e.g., the user checked the mobile device out at the startof the day, will use it during the day, and will return it at the end ofthe day).

WMS 119, in some embodiments, may maintain a work log for each userassociated with system 100. For example, WMS 119 may store informationassociated with each employee, including any assigned processes (e.g.,unloading trucks, picking items from a pick zone, rebin wall work,packing items), a user identifier, a location (e.g., a floor or zone ina fulfillment center 200), a number of units moved through the system bythe employee (e.g., number of items picked, number of items packed), anidentifier associated with a device (e.g., devices 119A-119C), or thelike. In some embodiments, WMS 119 may receive check-in and check-outinformation from a timekeeping system, such as a timekeeping systemoperated on a device 119A-119C.

3^(rd) party fulfillment (3PL) systems 121A-121C, in some embodiments,represent computer systems associated with third-party providers oflogistics and products. For example, while some products are stored infulfillment center 200 (as discussed below with respect to FIG. 2),other products may be stored off-site, may be produced on demand, or maybe otherwise unavailable for storage in fulfillment center 200. 3PLsystems 121A-121C may be configured to receive orders from FO system 113(e.g., through FMG 115) and may provide products and/or services (e.g.,delivery or installation) to customers directly. In some embodiments,one or more of 3PL systems 121A-121C may be part of system 100, while inother embodiments, one or more of 3PL systems 121A-121C may be outsideof system 100 (e.g., owned or operated by a third-party provider).

Fulfillment Center Auth system (FC Auth) 123, in some embodiments, maybe implemented as a computer system with a variety of functions. Forexample, in some embodiments, FC Auth 123 may act as a single-sign on(SSO) service for one or more other systems in system 100. For example,FC Auth 123 may enable a user to log in via internal front end system105, determine that the user has similar privileges to access resourcesat shipment and order tracking system 111, and enable the user to accessthose privileges without requiring a second log in process. FC Auth 123,in other embodiments, may enable users (e.g., employees) to associatethemselves with a particular task. For example, some employees may nothave an electronic device (such as devices 119A-119C) and may insteadmove from task to task, and zone to zone, within a fulfillment center200, during the course of a day. FC Auth 123 may be configured to enablethose employees to indicate what task they are performing and what zonethey are in at different times of day.

Labor management system (LMS) 125, in some embodiments, may beimplemented as a computer system that stores attendance and overtimeinformation for employees (including full-time and part-time employees).For example, LMS 125 may receive information from FC Auth 123, WMS 119,devices 119A-119C, transportation system 107, and/or devices 107A-107C.

The particular configuration depicted in FIG. 1A is an example only. Forexample, while FIG. 1A depicts FC Auth system 123 connected to FO system113, not all embodiments require this particular configuration. Indeed,in some embodiments, the systems in system 100 may be connected to oneanother through one or more public or private networks, including theInternet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like. In someembodiments, one or more of the systems in system 100 may be implementedas one or more virtual servers implemented at a data center, serverfarm, or the like.

FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is anexample of a physical location that stores items for shipping tocustomers when ordered. Fulfillment center (FC) 200 may be divided intomultiple zones, each of which are depicted in FIG. 2. These “zones,” insome embodiments, may be thought of as virtual divisions betweendifferent stages of a process of receiving items, storing the items,retrieving the items, and shipping the items. So while the “zones” aredepicted in FIG. 2, other divisions of zones are possible, and the zonesin FIG. 2 may be omitted, duplicated, or modified in some embodiments.

Inbound zone 203 represents an area of FC 200 where items are receivedfrom sellers who wish to sell products using system 100 from FIG. 1A.For example, a seller may deliver items 202A and 202B using truck 201.Item 202A may represent a single item large enough to occupy its ownshipping pallet, while item 202B may represent a set of items that arestacked together on the same pallet to save space.

A worker will receive the items in inbound zone 203 and may optionallycheck the items for damage and correctness using a computer system (notpictured). For example, the worker may use a computer system to comparethe quantity of items 202A and 202B to an ordered quantity of items. Ifthe quantity does not match, that worker may refuse one or more of items202A or 2028. If the quantity does match, the worker may move thoseitems (using, e.g., a dolly, a handtruck, a forklift, or manually) tobuffer zone 205. Buffer zone 205 may be a temporary storage area foritems that are not currently needed in the picking zone, for example,because there is a high enough quantity of that item in the picking zoneto satisfy forecasted demand. In some embodiments, forklifts 206 operateto move items around buffer zone 205 and between inbound zone 203 anddrop zone 207. If there is a need for items 202A or 202B in the pickingzone (e.g., because of forecasted demand), a forklift may move items202A or 202B to drop zone 207.

Drop zone 207 may be an area of FC 200 that stores items before they aremoved to picking zone 209. A worker assigned to the picking task (a“picker”) may approach items 202A and 202B in the picking zone, scan abarcode for the picking zone, and scan barcodes associated with items202A and 202B using a mobile device (e.g., device 119B). The picker maythen take the item to picking zone 209 (e.g., by placing it on a cart orcarrying it).

Picking zone 209 may be an area of FC 200 where items 208 are stored onstorage units 210. In some embodiments, storage units 210 may compriseone or more of physical shelving, bookshelves, boxes, totes,refrigerators, freezers, cold stores, or the like. In some embodiments,picking zone 209 may be organized into multiple floors. In someembodiments, workers or machines may move items into picking zone 209 inmultiple ways, including, for example, a forklift, an elevator, aconveyor belt, a cart, a handtruck, a dolly, an automated robot ordevice, or manually. For example, a picker may place items 202A and 202Bon a handtruck or cart in drop zone 207 and walk items 202A and 202B topicking zone 209.

A picker may receive an instruction to place (or “stow”) the items inparticular spots in picking zone 209, such as a particular space on astorage unit 210. For example, a picker may scan item 202A using amobile device (e.g., device 119B). The device may indicate where thepicker should stow item 202A, for example, using a system that indicatean aisle, shelf, and location. The device may then prompt the picker toscan a barcode at that location before stowing item 202A in thatlocation. The device may send (e.g., via a wireless network) data to acomputer system such as WMS 119 in FIG. 1A indicating that item 202A hasbeen stowed at the location by the user using device 1198.

Once a user places an order, a picker may receive an instruction ondevice 1198 to retrieve one or more items 208 from storage unit 210. Thepicker may retrieve item 208, scan a barcode on item 208, and place iton transport mechanism 214. While transport mechanism 214 is representedas a slide, in some embodiments, transport mechanism may be implementedas one or more of a conveyor belt, an elevator, a cart, a forklift, ahandtruck, a dolly, or the like. Item 208 may then arrive at packingzone 211.

Packing zone 211 may be an area of FC 200 where items are received frompicking zone 209 and packed into boxes or bags for eventual shipping tocustomers. In packing zone 211, a worker assigned to receiving items (a“rebin worker”) will receive item 208 from picking zone 209 anddetermine what order it corresponds to. For example, the rebin workermay use a device, such as computer 119C, to scan a barcode on item 208.Computer 119C may indicate visually which order item 208 is associatedwith. This may include, for example, a space or “cell” on a wall 216that corresponds to an order. Once the order is complete (e.g., becausethe cell contains all items for the order), the rebin worker mayindicate to a packing worker (or “packer”) that the order is complete.The packer may retrieve the items from the cell and place them in a boxor bag for shipping. The packer may then send the box or bag to a hubzone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,manually, or otherwise.

Hub zone 213 may be an area of FC 200 that receives all boxes or bags(“packages”) from packing zone 211. Workers and/or machines in hub zone213 may retrieve package 218 and determine which portion of a deliveryarea each package is intended to go to, and route the package to anappropriate camp zone 215. For example, if the delivery area has twosmaller sub-areas, packages will go to one of two camp zones 215. Insome embodiments, a worker or machine may scan a package (e.g., usingone of devices 119A-119C) to determine its eventual destination. Routingthe package to camp zone 215 may comprise, for example, determining aportion of a geographical area that the package is destined for (e.g.,based on a postal code) and determining a camp zone 215 associated withthe portion of the geographical area.

Camp zone 215, in some embodiments, may comprise one or more buildings,one or more physical spaces, or one or more areas, where packages arereceived from hub zone 213 for sorting into routes and/or sub-routes. Insome embodiments, camp zone 215 is physically separate from FC 200 whilein other embodiments camp zone 215 may form a part of FC 200.

Workers and/or machines in camp zone 215 may determine which routeand/or sub-route a package 220 should be associated with, for example,based on a comparison of the destination to an existing route and/orsub-route, a calculation of workload for each route and/or sub-route,the time of day, a shipping method, the cost to ship the package 220, aPDD associated with the items in package 220, or the like. In someembodiments, a worker or machine may scan a package (e.g., using one ofdevices 119A-119C) to determine its eventual destination. Once package220 is assigned to a particular route and/or sub-route, a worker and/ormachine may move package 220 to be shipped. In exemplary FIG. 2, campzone 215 includes a truck 222, a car 226, and delivery workers 224A and224B. In some embodiments, truck 222 may be driven by delivery worker224A, where delivery worker 224A is a full-time employee that deliverspackages for FC 200 and truck 222 is owned, leased, or operated by thesame company that owns, leases, or operates FC 200. In some embodiments,car 226 may be driven by delivery worker 224B, where delivery worker224B is a “flex” or occasional worker that is delivering on an as-neededbasis (e.g., seasonally). Car 226 may be owned, leased, or operated bydelivery worker 224B.

Referring to FIG. 3, a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems for training oneor more models for product categorization is shown. As illustrated inFIG. 3, system 300 may include a training data system 310, apre-processing system 320, a hierarchical model trainer system 330, andan image model trainer system 340, each of which may communicate with auser device 360 associated with a user 360A via a network 350. In someembodiments, training data system 310, pre-processing system 320,hierarchical model trainer system 330, and image model trainer system340 may communicate with each other and with the other components ofsystem 300 via a direct connection, for example, using a cable. In someother embodiments, system 300 may be a part of system 100 of FIG. 1A andmay communicate with the other components of system 100 (e.g., externalfront end system 103 or internal front end system 105) via network 350or via a direct connection, for example, using a cable. Training datasystem 310, pre-processing system 320, hierarchical model trainer system330, and image model trainer system 340 may each comprise a singlecomputer or may each be configured as a distributed computer systemincluding multiple computers that interoperate to perform one or more ofthe processes and functionalities associated with the disclosedexamples.

As shown in FIG. 3, training data system 310 may comprise a processor312, a memory 314, and a database 316. Pre-processing system 320 maycomprise a processor 322, a memory 324, and a database 326. Hierarchicalmodel trainer system 330 may comprise a processor 332, a memory 334, anda database 336. Image model trainer system 340 may comprise a processor342, a memory 344, and a database 346. Processors 312, 322, 332, and 342may be one or more known processing devices, such as a microprocessorfrom the Pentium™ family manufactured by Intel™ or the Turion™ familymanufactured by AMD™. Processors 312, 322, 332, and 342 may constitute asingle core or multiple core processor that executes parallel processessimultaneously. For example, processors 312, 322, 332, and 342 may uselogical processors to simultaneously execute and control multipleprocesses. Processors 312, 322, 332, and 342 may implement virtualmachine technologies or other known technologies to provide the abilityto execute, control, run, manipulate, store, etc. multiple softwareprocesses, applications, programs, etc. In another example, processors312, 322, 332, and 342 may include a multiple-core processor arrangementconfigured to provide parallel processing functionalities to allowtraining data system 310, pre-processing system 320, hierarchical modeltrainer system 330, and image model trainer system 340 to executemultiple processes simultaneously. One of ordinary skill in the artwould understand that other types of processor arrangements could beimplemented that provide for the capabilities disclosed herein.

Memories 314, 324, 334, and 344 may store one or more operating systemsthat perform known operating system functions when executed byprocessors 312, 322, 332, and 342, respectively. By way of example, theoperating system may include Microsoft Windows, Unix, Linux, Android,Mac OS, iOS, or other types of operating systems. Accordingly, examplesof the disclosed invention may operate and function with computersystems running any type of operating system. Memories 314, 324, 334,and 344 may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible computer readable medium.

Databases 316, 326, 336, and 346 may include, for example, Oracle™databases, Sybase™ databases, or other relational databases ornon-relational databases, such as Hadoop™ sequence files, HBase™, orCassandra™. Databases 316, 326, 336, and 346 may include computingcomponents (e.g., database management system, database server, etc.)configured to receive and process requests for data stored in memorydevices of the database(s) and to provide data from the database(s).Databases 316, 326, 336, and 346 may include NoSQL databases such asHBase, MongoDB™ or Cassandra™. Alternatively, databases 316, 326, 336,and 346 may include relational databases such as Oracle, MySQL andMicrosoft SQL Server. In some embodiments, databases 316, 326, 336, and346 may take the form of servers, general purpose computers, mainframecomputers, or any combination of these components.

Databases 316, 326, 336, and 346 may store data that may be used byprocessors 312, 322, 332, and 342, respectively, for performing methodsand processes associated with disclosed examples. Databases 316, 326,336, and 346 may be located in training data system 310, pre-processingsystem 320, hierarchical model trainer system 330, and image modeltrainer system 340, respectively, as shown in FIG. 3, or alternatively,it may be in an external storage device located outside of training datasystem 310, pre-processing system 320, hierarchical model trainer system330, and image model trainer system 340. Data stored in 316 may includeany suitable initial training data associated with products (e.g.,product identification number, highest category level, categorysublevels, product name, product image, product brand, productdescription, etc.), data stored in 326 may include any suitable dataassociated with pre-processed training data, data stored in 336 mayinclude any suitable data associated with training the hierarchicalmodel, and data stored in 346 may include any suitable data associatedwith training the image model.

User device 360 may be a tablet, mobile device, computer, or the like.User device 360 may include a display. The display may include, forexample, liquid crystal displays (LCD), light emitting diode screens(LED), organic light emitting diode screens (OLED), a touch screen, andother known display devices. The display may show various information toa user. For example, it may display an online platform for entering orgenerating training data, including an input text box for internal users(e.g., employees of an organization that owns, operates, or leasessystem 100) or external users to enter training data, including productinformation (e.g., product identification number, highest categorylevel, category sublevels, product name, product image, product brand,product description, etc.). User device 360 may include one or moreinput/output (I/O) devices. The I/O devices may include one or moredevices that allow user device 360 to send and receive information fromuser 360A or another device. The I/O devices may include variousinput/output devices, a camera, a microphone, a keyboard, a mouse-typedevice, a gesture sensor, an action sensor, a physical button, anoratory input, etc. The I/O devices may also include one or morecommunication modules (not shown) for sending and receiving informationfrom training data system 310, pre-processing system 320, hierarchicalmodel trainer system 330, or image model trainer system 340 by, forexample, establishing wired or wireless connectivity between user device360 and network 350.

Training data system 310 may receive initial training data includingproducts associated with one or more categories. Training data system310 may collect training data using a combination of different methods.Training data collection methods may include human labeled data, virtualproduct data, mapping guideline keyword data, or selection of liveproducts. For example, training data system 310 may receive initialtraining data from internal users (e.g., employees of an organizationthat owns, operates, or leases system 100) via internal front end system105.

Human labeled data may include user 360A manually inputting productinformation (e.g., product identification number, highest categorylevel, category sublevels, product name, product image, product brand,product description, etc.) for each training data point for at least onetraining data point.

Virtual product data may include automatically generating augmentedtraining data using existing categories. For example, training datasystem 310 may automatically generate at least one training data pointincluding a product name (e.g., women's short pants) using keywordsobtained from existing categories and subcategories (e.g., fashion,women's fashion, women's clothing, pants, short pants). Generatingvirtual product data may improve robustness of the model(s) to betrained since the product name is obvious from the keywords and shouldnot be mis-categorized.

Mapping guideline keyword data may include automatically generatingaugmented training data using live products. For example, training datasystem 310 may automatically generate at least one training data pointby searching database 316 for a live product that is mapped to at leastone keyword. If the live product is already mapped to a productidentification number and contains the at least one keyword in one ofits associated category, training data system 310 considers that liveproduct to be correctly categorized and generates a new training datapoint that is a duplicate of the live product. Generating mappingguideline keyword data may improve robustness of the model(s) to betrained since it increases the amount of correctly categorized trainingdata to feed into the model(s) to be trained.

Selection of live products may include automatically generatingaugmented training data by randomly collecting a maximum of ten productsfrom a live product list and their associated categories. For example,training data system 310 may determine that some categories do not haveany associated training data. Training data system 310 may automaticallygenerate training data by randomly collecting a maximum of ten liveproducts that are labeled with the categories that do not have anyassociated training data, and generate new training data points that areduplicates of the collected live products. Selection of live productsmay improve the quality of the model(s) to be trained by providing amore complete training data set.

Pre-processing system 320 may receive the initial training datacollected by training data system 310 and generate synthesized trainingdata by pre-processing the initial training data. The text-based initialtraining data may be pre-processed using any combination of methods,which include stop word elimination, keyword tokenization, deduplicationof keywords, and augmentation of the initial training data, and theimage-based initial training data may be pre-processed using imageaugmentation techniques (e.g., PyTorch).

Pre-processing system 320 may eliminate stop words by referencing adictionary of stop words stored in database 326. Stop words may includewords associated with the training data that are not relevant to productcharacteristics and, thus, are not necessary for product categorization.For example, stop words may include “sale,” “discount,” or “freedelivery.” Stop word elimination may increase robustness of the model(s)to be trained by removing superfluous words that slow down the modeltraining process from the training data set.

Pre-processing system 320 may tokenize keywords by referencing a tokendictionary stored in database 326 and implementing an Aho-Corasickalgorithm to determine whether or not to split a keyword into multiplekeywords. For example, keywords written in certain languages (such asKorean) may be stored as a single string of text without spaces. (Afluent speaker would understand that this string of text may be splitinto various combinations of words.) Pre-processing system 320 mayimplement an Aho-Corasick algorithm, which is a dictionary-matchingalgorithm that locates elements of a finite set of strings (e.g., the“dictionary”) within an input text. The algorithm matches all thestrings simultaneously so that pre-processing system 320 may generatesynthesized training data by collecting the actual keywords of the inputtext while removing “split” words that are not listed in the storeddictionary. Keyword tokenization may increase robustness of the model(s)to be trained by removing superfluous words that slow down the modeltraining process from the training data set.

Pre-processing system 320 may deduplicate keywords by identifyingkeywords in the training set and removing any duplicates of the existingkeywords. Keyword deduplication may increase the balance of productcategorization results by removing repeating keywords.

Pre-processing system 320 may augment the initial training data byduplicating some training data and either removing irrelevantalphanumeric characters from the duplicated data or randomly removingkeywords from the duplicated data. For example, when the training dataand product categories are mostly written in Korean, pre-processingsystem 320 may duplicate the initial training data and remove Englishand numerical characters from the duplicated training data.Pre-processing system 320 may then add this augmented data to thetraining data set to enforce increased model learning of Koreancharacters. Pre-processing system 320 may also duplicate the initialtraining data and randomly remove keywords from the duplicated trainingdata. Pre-processing system 320 may then add this augmented data to thetraining data set to increase robustness of the model(s) to be trainedby reducing the likelihood of overfitting the model(s).

Pre-processing system 320 may augment the image-based initial trainingdata by rotating the images, shifting the images, flipping the images,adding noise to the images, blurring the images, etc. For example,pre-processing system 320 may duplicate each image of the initialtraining data set multiple times and rotate the duplicated images invarious orientations. Pre-processing system 320 may increase therobustness of the model(s) to be trained by adding the rotatedduplicated images to the training data set.

In another example, pre-processing system 320 may duplicate each imageof the initial training data set multiple times and shift the duplicatedimages in various off-center positions. Pre-processing system 320 mayincrease the robustness and generalization of the model(s) to be trainedby adding the shifted duplicated images to the training data set.

In another example, pre-processing system 320 may duplicate each imageof the initial training data set multiple times and flip the duplicatedimages in various orientations. Pre-processing system 320 may increasethe generalization of the model(s) to be trained by adding the flippedduplicated images to the training data set.

In another example, pre-processing system 320 may duplicate each imageof the initial training data set multiple times and add random noise tothe duplicated images. Pre-processing system 320 may help the model(s)learn to separate signal from noise in an image by adding the duplicatedimages with noise to the training data set, thereby increasing therobustness of the model(s) to be trained.

In another example, pre-processing system 320 may duplicate each imageof the initial training data set multiple times and blur (e.g., changethe quality of) the duplicated images in various amounts. Pre-processingsystem 320 may increase the robustness of the model(s) to be trained byadding the blurred duplicated images to the training data set.

Hierarchical model trainer system 330 may receive the text-basedsynthesized training data generated by pre-processing system 320 andimage model trainer system 340 may receive the image-based synthesizedtraining data generated by pre-processing system 320. Hierarchical modeltrainer system 330 and image model trainer 340 may generate and train atleast one hierarchical model and at least one image model, respectively,using the received synthesized data for product categorization.

Referring to FIG. 4, sample training data 400 for product categorizationis shown. As illustrated in FIG. 4, training data may include multiplecells that are associated with a single product. For example, trainingdata may include a product identification number 410, a high levelcategory 412, one or more subcategories 414, a product name 416, a brand418, or a product image URL 420. Training data may be obtained using anyof the embodiments disclosed herein.

Referring to FIG. 5, a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems for productcategorization using one or more models is shown. As illustrated in FIG.5, system 500 may include a product category predictor system 520, whichmay communicate with a user device 560 (which may be the same as userdevice 360 of FIG. 3) associated with a user 560A (which may be the sameuser 360A of FIG. 3) via a network 550. In some embodiments, productcategory predictor 520 may communicate with the other components ofsystem 500 via a direct connection, for example, using a cable. In someother embodiments, system 500 may be a part of system 100 of FIG. 1A orsystem 300 of FIG. 3 and may communicate with the other components ofsystems 100 or 300 via network 550 or via a direct connection, forexample, using a cable. Product category predictor 520 may comprise asingle computer or may be configured as a distributed computer systemincluding multiple computers that interoperate to perform one or more ofthe processes and functionalities associated with the disclosedexamples.

As shown in FIG. 5, product category predictor 520 may comprise aprocessor 522, a memory 524, and a database 526. Processor 522 may beone or more known processing devices, such as a microprocessor from thePentium™ family manufactured by Intel™ or the Turion™ familymanufactured by AMD™. Processor 522 may constitute a single core ormultiple core processor that executes parallel processes simultaneously.For example, processor 522 may use logical processors to simultaneouslyexecute and control multiple processes. Processor 522 may implementvirtual machine technologies or other known technologies to provide theability to execute, control, run, manipulate, store, etc. multiplesoftware processes, applications, programs, etc. In another example,processor 522 may include a multiple-core processor arrangementconfigured to provide parallel processing functionalities to allowproduct category predictor 520 to execute multiple processessimultaneously. One of ordinary skill in the art would understand thatother types of processor arrangements could be implemented that providefor the capabilities disclosed herein.

Memory 524 may store one or more operating systems that perform knownoperating system functions when executed by processor 522, respectively.By way of example, the operating system may include Microsoft Windows,Unix, Linux, Android, Mac OS, iOS, or other types of operating systems.Accordingly, examples of the disclosed invention may operate andfunction with computer systems running any type of operating system.Memory 524 may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible computer readable medium.

Database 526 may include, for example, Oracle™ databases, Sybase™databases, or other relational databases or non-relational databases,such as Hadoop™ sequence files, HBase™, or Cassandra™. Database 526 mayinclude computing components (e.g., database management system, databaseserver, etc.) configured to receive and process requests for data storedin memory devices of the database(s) and to provide data from thedatabase(s). Database 526 may include NoSQL databases such as HBase,MongoDB™ or Cassandra™. Alternatively, database 526 may includerelational databases such as Oracle, MySQL and Microsoft SQL Server. Insome embodiments, database 526 may take the form of servers, generalpurpose computers, mainframe computers, or any combination of thesecomponents.

Database 526 may store data that may be used by processor 522 forperforming methods and processes associated with disclosed examples.Database 526 may be located in product category predictor 520 as shownin FIG. 5, or alternatively, it may be in an external storage devicelocated outside of product category predictor 520. Data stored in 526may include any information associated with products provided by sellers(e.g., product name, attribute values, manufacturer, brand, modelnumber, etc.).

User device 560 may be a tablet, mobile device, computer, or the like.User device 560 may include a display. The display may include, forexample, liquid crystal displays (LCD), light emitting diode screens(LED), organic light emitting diode screens (OLED), a touch screen, andother known display devices. The display may show various information toa user. For example, it may display an online platform for enteringproduct information, including an input text box for sellers to enterproduct information (e.g., product name, attribute values, manufacturer,brand, model number, etc.). User device 560 may include one or moreinput/output (I/O) devices. The I/O devices may include one or moredevices that allow user device 560 to send and receive information fromuser 560A or another device. The I/O devices may include variousinput/output devices, a camera, a microphone, a keyboard, a mouse-typedevice, a gesture sensor, an action sensor, a physical button, anoratory input, etc. The I/O devices may also include one or morecommunication modules (not shown) for sending and receiving informationfrom product category predictor 520 by, for example, establishing wiredor wireless connectivity between user device 560 and network 550.

Product category predictor 520 may receive input data 510 from user 560A(e.g., seller(s)) via network 550. For example, user 560A may use userdevice 560 to communicate with seller portal 109 and register at leastone product. User 560A may be prompted to enter a concatenated textstring including the product name, attribute values, manufacturer,brand, and model number. User 560A may also be prompted to enter anumber of categories N and a number of categories M that productcategory predictor 520 may predict for a given product. The input 510including the concatenated text string and the numbers of categories Nand M may be the only input data.

Product category predictor 520 may identify the keywords from input 510and use a library (e.g., fastText) to transform the keywords into vectorrepresentations. Product category predictor 520 may use the library tolearn a representation for each keyword's character n-gram. Each keywordmay then be represented as a bag of character n-grams and the overallword embedding is a sum of the character n-grams. For example, aninternal user or external user (e.g., user 560A) may manually set orproduct category predictor 520 may automatically set the n-gram to 3, inwhich case the vector for the word “where” would be represented by a sumof trigrams: <wh, whe, her, ere, re>, where the brackets <, > areboundary symbols that denote the beginning and end of a word. After eachword is represented as a sum of n-grams, a latent text embedding isderived as an average of the word embedding, at which point the textembedding may be used by product category predictor 520 to predict thelabel. This process may be advantageous in identifying rare keywords orkeywords that were not included in the training data set.

Product category predictor 520 may use a trained hierarchical model(e.g., hierarchical model trainer system 330 of FIG. 3) to predictcategories associated with product information provided by user 560A. Insome embodiments, the product information may indicate that the productis incorrectly categorized, in which case product category predictor 520may predict the correct categories for that product. The hierarchicalmodel may use input 510 to predict N categories of the uncategorizedproduct by first calculating a probability score for each node on thefirst layer for the uncategorized product. The probability scoreindicates the probability that the uncategorized product belongs to theassociated category. The hierarchical model may then determine a set ofn nodes on the first layer (e.g., layer 610 of FIG. 6) with the highestn probability scores. For example, the top layer may include fivecategories and, where n=3, the hierarchical model may determine thethree nodes (or categories) on the first layer with the three highestprobability score.

The hierarchical model may then calculate a probability score for a setof nodes on the second layer (e.g., second layer 620 of FIG. 6), wherethe set of nodes on the second layer are subcategories of the n nodes(or categories) with the highest n probability scores. The hierarchicalmodel may then determine a set of m nodes on the second layer with thehighest m scores. For example, where m=3, the hierarchical model maydetermine the three nodes (or categories) on the second layer with thethree highest scores. The hierarchical model may continue this processrecursively for any number of categories. If a node in the final layer(e.g., layer 630 of FIG. 6) has more nodes beyond the final layer, thehierarchical model may flatten the structure of that node so that afinal, total probability score may be calculated.

The hierarchical model may calculate a total probability based on theprobability scores of the nodes in each layer. For example, if thehierarchical model has three layers based on the highest threeprobability scores of each previous layer, then hierarchical model has27 final category candidates in the third layer to choose from (layer1's top 3·layer 2's top 3·layer 3's top 3=27 candidates). The totalprobability score for each of the 27 candidates (e.g., node 631 of FIG.6) may be calculated by multiplying its probability score in each layer(e.g., layer 1 score·layer 2 score·layer 3 score=total probability scorefor one node). The hierarchical model may then determine the Ncategories with the highest N total probability scores.

Product category predictor 520 may use a trained image model (e.g.,image model trainer 340 of FIG. 3) to predict M categories of a firstuncategorized product. Product category predictor 520 may calculate aprobability score for each category using the trained image model anddetermine the highest M categories with the highest M probabilityscores.

Product category predictor 520 may average the total probability scoreof the N categories with the total probability score of thecorresponding M categories. For example, for a particular category, thetotal probability score determined by the hierarchical model for thatcategory is averaged with the total probability score determined by theimage model for that same category. N and M may not necessarily be thesame number of categories. For example, if the hierarchical modeldetermines a score for one of the N categories and the image model doesnot have a score for that same category, the total probability scorefrom the image model for that category is zero. Product categorypredictor 520 may determine that the categories with the highest averagescores are the most relevant categories for the uncategorized product,and subsequently sort the uncategorized product into the one or more ofthe N or M categories having the highest averaged total probabilityscore.

Referring to FIG. 6, a sample hierarchical model 600 for productcategorization is shown. For example, hierarchical model 600 may includea root node 601 and a first layer 610, which includes a plurality ofnodes 611 representing different product categories. Hierarchical model600 may also include a second layer 620, which includes a plurality ofnodes 621 representing different product subcategories of nodes 611.Hierarchical model 600 may also include a third layer 630, whichincludes a plurality of nodes 631 representing different productsubcategories of nodes 621. Hierarchical model 600 may include anynumber of layers and nodes and is not limited to the embodimentdepicted.

Referring to FIG. 7, a process for product categorization is shown.While in some embodiments one or more of the systems depicted in FIGS. 3and 5 may perform several of the steps described herein, otherimplementations are possible. For example, any of the systems andcomponents (e.g., system 100, etc.) described and illustrated herein mayperform the steps described in this disclosure.

In step 701, training data system 310 may receive, from user 360A overnetwork 350, initial training data (e.g., product identification 410,high level category 412, subcategories 414, product name 416, brand 418,or product image URL 420 of FIG. 4) including products associated withone or more categories. For example, initial training data may includeany combination of human labeled data, automatically generated virtualproduct data, automatically generated mapping guideline keyword data, orautomatic selection of live products. Training data system 310 mayreceive initial training data from internal users (e.g., employees of anorganization that owns, operates, or leases system 100) via internalfront end system 105.

In step 703, pre-processing system 320 may pre-process the initialtraining data received from training data system 310 to generatesynthesized training data. For example, text-based initial training datamay be pre-processed using any combination of methods, which includestop word elimination, keyword tokenization, deduplication of keywords,and augmentation of the initial training data, and the image-basedinitial training data may be pre-processed using image augmentationtechniques (e.g., PyTorch).

In step 705, hierarchical model trainer system 330 may receive thetext-based synthesized training data generated by pre-processing system320 and image model trainer system 340 may receive the image-basedsynthesized training data generated by pre-processing system 320.Hierarchical model trainer system 330 and image model trainer 340 maygenerate and train at least one hierarchical model and at least oneimage model, respectively, using the received synthesized data forproduct categorization. Going back to exemplary FIG. 6, the hierarchicalmodel may contain at least two layers, 610 and 620, of nodes, 611 and621, below root node 601.

In step 707, product category predictor 520 may receive informationassociated with a first uncategorized product. For example, user 560Amay use user device 560 to communicate with seller portal 109 andregister at least one product. User 560A may be prompted to enter aconcatenated text string including the product name, attribute values,manufacturer, brand, and model number.

In step 709, product category predictor 520 may receive a request topredict a set of N categories with the highest N total probabilityscores. For example, user 560A may be prompted to enter a number ofcategories N that product category predictor 520 may predict for a givenproduct.

In step 711, product category predictor 520 may use the hierarchicalmodel to predict N categories of the first uncategorized product byfirst calculating a probability score for each node on the first layerfor the uncategorized product. The probability score indicates theprobability that the uncategorized product belongs to the associatedcategory. The hierarchical model may then determine a set of n nodes onthe first layer (e.g., layer 610 of FIG. 6) with the highest nprobability scores. For example, the top layer may include fivecategories and, where n=3, the hierarchical model may determine thethree nodes (or categories) on the first layer with the three highestprobability score. The hierarchical model may then calculate aprobability score for a set of nodes on the second layer (e.g., secondlayer 620 of FIG. 6), where the set of nodes on the second layer aresubcategories of the n nodes (or categories) with the highest nprobability scores. The hierarchical model may then determine a set of mnodes on the second layer with the highest m scores. For example, wherem=3, the hierarchical model may determine the three nodes (orcategories) on the second layer with the three highest scores. Thehierarchical model may continue this process recursively for any numberof categories. The hierarchical model may calculate a total probabilitybased on the probability scores of the m nodes with the highest m scoresand respective n nodes in the first layer. The hierarchical model maythen determine the N categories with the highest N total probabilityscores.

In step 713, product category predictor 520 may subsequently sort theuncategorized product into the one or more of the N categoriesassociated with the nodes from the first and second layers having thehighest total probability scores.

In step 715, product category predictor 520 may display the sorted firstuncategorized product and its associated N categories on a user device(e.g., user device 560) associated with a user (e.g., user 560A). Forexample, product category predictor 520 may prepare or modify a web pageto include data associated with the first uncategorized product and oneor more associated categories.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented system for Al-basedproduct categorization, the system comprising: a memory storinginstructions; and at least one processor configured to execute theinstructions to: retrieve initial training data including productsassociated with one or more categories; pre-process the initial trainingdata to generate synthesized training data, wherein pre-processing theinitial training data comprises duplicating the initial training dataand randomly removing keywords from the duplicated initial trainingdata; generate a hierarchical model using the synthesized training data,the hierarchical model containing at least two layers of nodes below aroot node; receive information associated with a first uncategorizedproduct; receive a request to predict a set of N categories with thehighest N total probability scores; predict, using the hierarchicalmodel, N categories of the first uncategorized product, by: calculatinga probability score for each node on the first layer for the firstuncategorized product, determining a set of n nodes on the first layerwith the highest n probability scores, calculating a probability scorefor a set of nodes on the second layer, the set of nodes being below thedetermined set of n nodes with the highest n probability scores,determining a set of m nodes on the second layer with the highest mscores, calculating a total probability score based on the probabilityscores of the m nodes with the highest m scores and respective n nodesin the first layer, and determining the N categories with the highest Ntotal probability scores; sort the first uncategorized product into theN categories associated with the nodes from the first and second layershaving the highest total probability scores; display the sorted firstuncategorized product and its associated N categories on a user deviceassociated with a user.
 2. The system of claim 1, wherein the initialtraining data includes images associated with one or more categories. 3.The system of claim 2, wherein the instructions further cause theprocessor to generate an image model using the synthesized trainingdata.
 4. The system of claim 1, wherein the initial training datacomprises at least one of human labeled data, virtual product data,mapping guideline keyword data, or selection of live products.
 5. Thesystem of claim 1, wherein pre-processing the initial training datacomprises removing stop words from the initial training data.
 6. Thesystem of claim 1, wherein pre-processing the initial training datacomprises tokenizing the initial training data.
 7. The system of claim1, wherein pre-processing the initial training data comprisesde-duplicating the initial training data.
 8. The system of claim 1,wherein pre-processing the initial training data comprises duplicatingthe initial training data and removing irrelevant alphanumericcharacters from the duplicated initial training data.
 9. The system ofclaim 1, wherein the received information associated with the firstuncategorized product indicates that the first uncategorized product isincorrectly categorized.
 10. A method for categorizing products usingAl, the method comprising: retrieving initial training data includingproducts associated with one or more categories; pre-processing theinitial training data to generate synthesized training data, whereinpre-processing the initial training data comprises duplicating theinitial training data and randomly removing keywords from the duplicatedinitial training data; generating a hierarchical model using thesynthesized training data, the hierarchical model containing at leasttwo layers of nodes below a root node; receiving information associatedwith a first uncategorized product; receiving a request to predict a setof N categories with the highest N total probability scores; predicting,using the hierarchical model, N categories of the first uncategorizedproduct, by: calculating a probability score for each node on the firstlayer for the first uncategorized product, determining a set of n nodeson the first layer with the highest n probability scores, calculating aprobability score for a set of nodes on the second layer, the set ofnodes being below the determined set of n nodes with the highest nprobability scores, determining a set of m nodes on the second layerwith the highest m scores, calculating a total probability score basedon the probability scores of the m nodes with the highest m scores andrespective n nodes in the first layer, and determining the N categorieswith the highest N total probability scores; and sorting the firstuncategorized product into the N categories associated with the nodesfrom the first and second layers having the highest total probabilityscores.
 11. The method of claim 10, wherein the initial training dataincludes images associated with one or more categories.
 12. The methodof claim 11, wherein the method further comprises generating an imagemodel using the synthesized training data.
 13. The method of claim 10,wherein the initial training data comprises at least one of humanlabeled data, virtual product data, mapping guideline keyword data, orselection of live products.
 14. The method of claim 10, whereinpre-processing the initial training data comprises removing stop wordsfrom the initial training data.
 15. The method of claim 10, whereinpre-processing the initial training data comprises tokenizing theinitial training data.
 16. The method of claim 10, whereinpre-processing the initial training data comprises de-duplicating theinitial training data.
 17. The method of claim 10, whereinpre-processing the initial training data comprises duplicating theinitial training data and removing irrelevant alphanumeric charactersfrom the duplicated initial training data.
 18. A computer-implementedsystem for Al-based product categorization, the system comprising: amemory storing instructions; and at least one processor configured toexecute the instructions to: retrieve initial training data includingproducts and images associated with one or more categories; pre-processthe initial training data to generate synthesized training data, whereinpre-processing the initial training data comprises duplicating theinitial training data and randomly removing keywords from the duplicatedinitial training data; generate a hierarchical model using thesynthesized training data, the hierarchical model containing at leasttwo layers of nodes below a root node; generate an image model using thesynthesized training data; receive information associated with a firstuncategorized product; receive a request to predict a set of Ncategories with the highest N total probability scores and a request topredict a set of M categories with the highest M total probabilityscores; predict, using the hierarchical model, N categories of the firstuncategorized product, by: calculating a probability score for each nodeon the first layer for the first uncategorized product, determining aset of n nodes on the first layer with the highest n probability scores,calculating a probability score for a set of nodes on the second layer,the set of nodes being below the determined set of n nodes with thehighest n probability scores, determining a set of m nodes on the secondlayer with the highest m scores, calculating a total probability scorebased on the probability scores of the m nodes with the highest m scoresand respective n nodes in the first layer, and determining the Ncategories with the highest N total probability scores; predict, usingthe image model, M categories of the first uncategorized product, by:calculating a probability score for the first uncategorized product, anddetermining the M categories with the highest M total probabilityscores; average the total probability score of the N categories with theM categories; sort the first uncategorized product into the category ofthe N or M categories having the highest averaged total probabilityscore; and display the sorted first uncategorized product and itsassociated N or M categories on a user device associated with a user.